This paper investigates improving supervised
word segmentation accuracy with unlabeled
data. Both large-scale in-domain data and
small-scale document text are considered. We
present a unified solution to include features
derived from unlabeled data to a discriminative
learning model. For the large-scale data,
we derive string statistics from Gigaword to
assist a character-based segmenter. In addition,
we introduce the idea about transductive,
document-level segmentation, which is designed
to improve the system recall for out-ofvocabulary
(OOV) words which appear more
than once inside a document. Novel features1
result in relative error reductions of 13.8% and
15.4% in terms of F-score and the recall of
OOV words respectively.